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IC-Service: A Service-Oriented Approach to the Development of Recommendation Systems. Aliaksandr Birukou, Enrico Blanzieri, Vincenzo D'Andrea, Paolo Giorgini, Natallia Kokash, Alessio Modena. Introduction. Recommendation systems Service-Oriented Computing Implicit Culture

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ic service a service oriented approach to the development of recommendation systems

IC-Service: A Service-Oriented Approach to the Development of Recommendation Systems

Aliaksandr Birukou, Enrico Blanzieri, VincenzoD'Andrea, Paolo Giorgini, Natallia Kokash, Alessio Modena

ACM SAC, Seoul, Korea

introduction
Introduction
  • Recommendation systems
  • Service-Oriented Computing
  • Implicit Culture
  • System for Implicit Culture Support (SICS)
  • SICS Architecture
    • Main modules
    • Configuration
  • Applications
    • Web service discovery
  • Conclusions
  • References

ACM SAC, Seoul, Korea

recommendation systems
Recommendation systems
  • Prune large information spaces in searching for items of interest
  • Examples
    • movies (MovieLens),
    • music (JUKE-BOX),
    • books (Amazon),
    • hotels (TripAdvisor)
  • Meta-recommendation systems
    • Work with data from multiple (heterogeneous) information sources
    • MetaLens [Schafer et al., 2002]

ACM SAC, Seoul, Korea

service oriented computing
Service

Registry

Publish

Bind

Find

Service

Client

Service

Provider

Service-oriented computing

Web service

description

  • Requirements for a recommendation service:
    • Use in various application domains
    • Ability to store heterogeneous client data
    • Adaptability to the needs of a particular client
    • Ability to process data according to the domain specific rules

Service-oriented

application

Web service

ACM SAC, Seoul, Korea

implicit culture ic motivation and goals
Implicit Culture (IC): motivation and goals
  • Communities of human/artificial agents have knowledge specific to their activities, i.e., community culture
  • The knowledge is often implicit and highly personalized
  • Encourage a newcomer to behave according to a community culture
  • Transfer knowledge implicitly (without special efforts for its analysis and description)
  • http://www.dit.unitn.it/~implicit
  • [Blanzieri et al., 2001]

ACM SAC, Seoul, Korea

ic definitions
Extract

actions performed

in different situations

Suggest

actions in

a given situation

Observe

agents’ actions

IC definitions
  • Action – something that can be done
  • Agent (actor) – somebody or something performing an action
  • Object – something that passively participate in the action
  • Situation – a state of the world faced by the agent. Includes a set of objects and a set of possible actions
  • Culture – a usual behavior of the group of agents
  • Group G – group of agents which behaviour is observed
  • Group G'– group of agents who require recommendations
  • Implicit Culture relation – situations in which agents of the group Gbehave similarly to agents of the group G'
  • System for Implicit Culture Support (SICS) – a system which tries to establish IC relation

ACM SAC, Seoul, Korea

system for implicit culture support sics
System for Implicit Culture Support (SICS)

Produce a theory about common user behavior

Produce recommendation about action

Stores information about actions

ACM SAC, Seoul, Korea

sics architecture
SICS Architecture
  • SICS Core
    • SICS layer

infers theory rules and recommends actions

    • Configuration and storage layer

manages theory

  • SICS Remote Module

defines protocols for information exchange with the client

  • SICS Remote Client

provides a simple interface for remote clients

ACM SAC, Seoul, Korea

storage module
Storage Module
  • Observations
    • Agents (1…N),
    • Actions (1),
    • Objects (0…N),
    • Attributes (0…N)
    • Scenes (1…N)
      • no agents
      • no timestamps
  • Theory rules
    • if consequent (predicates) then antecedent (predicates)
    • Predicates:
      • Conditions on observations (action- predicates)
      • Conditions on time (temporal-predicates)

ACM SAC, Seoul, Korea

inductive module
Inductive Module
  • Analyses observations and generates theory rules for an actor or a group of actors
  • “Apriori” algorithm for mining association rules [Agrawal & Srikant, 1994]
    • A transaction is a sequence of executed actions A1,…,AN (can be obtained from observations using timestamps)
    • An association rule is an implicationof the form A1 A2 where A1, A2 are actions, A1 A2
    • The rule holds with confidencec if c% of transactions that contain A1 also contain A2
    • The rule A1 A2 has support s in the transaction set s% of transactions contain A1 A2
    • Generate association rules that have support and confidence greater than predefined minimum support and minimum confidence.

ACM SAC, Seoul, Korea

composer module
Composer Module
  • Cultural Action Finder (CAF)
    • Matches actions executed by agents from group G’ with antecedents of the theory rules
      • Matching algorithms
    • Returns consequences of the theory rules (cultural actions)
  • Scene producer
    • Finds a set of agents that have performed actions similar to a cultural action for the agent X
    • Selects a set of agents similar to an agent X and a set of scenes S in which they have performed the actions
    • Select and propose to X a scene from S

ACM SAC, Seoul, Korea

instance configuration
Instance Configuration
  • Composer constants:
    • Similarity threshold
    • Number of nearest neighbors
    • Return all scenes or only the best
    • Max number of observations
    • Names of groups G and G’
  • Configuration of similarity functions:
    • Rules for calculating similarity among observations
    • Similarity weights for elements (names and values)
      • exceptions, instants and default
    • Case sensitive or not
    • Regular expressions
  • Inductive Module constants

ACM SAC, Seoul, Korea

applications
Applications
  • Prototypes:
    • Recommending Web links [Birukou et al., 2005]
    • Recommending scientific publications
  • Quality-based Indexing of Web Information (QUIEW) http://quiew.itc.it/
  • Supporting Polymerase Chain Reaction (PCR) experiments [Mullis et al., 1986] [Sarini et al., 2004]
  • Software patterns selection
  • Web service discovery

ACM SAC, Seoul, Korea

web service ws discovery
Web Service (WS) discovery
  • Meeting functionality required by a user with specifications of existing web services
    • Problems: incomplete specifications, broken links, unfair providers…
  • Choosing a service with good quality characteristics
    • Problems: often QoS data are not available, some of them are context-dependent…
  • Implicit Culture approach
    • Analyze which web services have been previously used for similar problems by clients with similar interests
    • Use up-to-date information to improve service discovery and QoS-driven selection

ACM SAC, Seoul, Korea

a system for ws discovery
A system for WS discovery

Search

process

Monitoring

process

ACM SAC, Seoul, Korea

ws discovery in terms of ic
WS discovery in terms of IC
  • Observations
    • Actors
      • Applications (application name, user name, location)
      • Users (user name, location)
    • Objects
      • Operations (operation name, web service name)
      • Inputs/Outputs (parameter name, parameter value)
      • Requests (goals, operations, inputs/outputs)
    • Actions
      • Invoke (timestamp, operation, input)
      • Get response (timestamp, operation, output, response time)
      • Raise exception (timestamp, operation, exception type, input)
      • Provide feedback (timestamp, QoS parameters)
      • Submit request (timestamp, request)
  • Rules
    • if submit request(request) then invoke(operation-X(service-Y), request).
  • Similarity measures:
    • Vector Space Model (VSM)
      • Term Frequency- Inverse Document Frequency (TF-IDF) metric
    • WordNet-based semantic similarity measure

ACM SAC, Seoul, Korea

a system for ws discovery experimental results
VSM

WordNet

A system for WS discovery: experimental results
  • 20 web services (http://www.xMethods.com) divided into 5 categories [Kokash et al., 2007]
  • 4 clients submit 100 requests

ACM SAC, Seoul, Korea

conclusions
Conclusions
  • Ubiquity
    • The IC-service can be accessed from any workplace
  • Reusability
    • A unique solution for various distributed communities
  • Integration
    • The knowledge transfer between communities is facilitated
  • Scalability
    • 100000 observations of 100 users for one instance
    • Composition of several IC-Services is possible
  • Portability
    • XML storage
  • Customization
    • Ability of runtime configuring of theory rules…

ACM SAC, Seoul, Korea

references
References
  • [Schafer et al., 2002] J. B. Schafer, J. A. Konstan, and J. Riedl. Meta-recommendation systems: user-controlled integration of diverse recommendations. In Proc. of the Int. Conference on Information and Knowledge Management, pages 43-51. ACM Press, 2002.
  • [Blanzieri et al., 2001] E. Blanzieri, P. Giorgini, P. Massa, and S. Recla. Implicit culture for multi-agent interaction support. In CooplS: Proc. of the 9th Int. Conference on Cooperative Information Systems, volume 2172 of LNCS, pages 27-39. Springer, 2001.
  • [Birukov et al., 2005] A. Birukov, E. Blanzieri, and P. Giorgini. Implicit: An agent-based recommendation system for web search. In AAMAS: Proc. of the 4th Int. Joint Conference on Autonomous Agents and Multiagent Systems, pages 618-624. ACM Press, 2005.
  • [Mullis et al., 1986] K. B. Mullis, F. A. Faloona, S. Scharf, R. K. Saiki, G. Horn, H. A. Erlich. Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. In Cold Spring Harbor Symposia on Quantitative Biology, volume 51, pages 263-273, 1986.
  • [Sarini et al., 2004] M. Sarini, E. Blanzieri, P. Giorgini, C. Moser. From actions to suggestions: supporting the work of biologists through laboratory notebooks. In COOP: Proc. of 6th Int. Conference on the Design of Cooperative Systems, pages 131-146. IOS Press, 2004.
  • [Agrawal & Srikant, 1994] R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB: Proc. of the 20th Int. Conference on Very Large Data Bases, pages 487-499. Morgan Kaufmann, 1994.
  • [Kokash et al., 2007] N. Kokash, A. Birukou, V. D'Andrea: Web service discovery based on past user experience. In: International Conference on Business Information Systems (BIS), to appear, Springer (2007)

ACM SAC, Seoul, Korea

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